ArticleFoods (Basel, Switzerland)2024
Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis.
Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Postharvest Fruit Grading Technologies and Equipment: A Review.Foods (Basel, Switzerland) · 2026Review
- Morphological and Pomological Characterization of Indigenous Pear Genotypes (Food science & nutrition · 2026Article
- Hyperspectral imaging with machine learning for non-destructive prediction of quality attributes in Lanzhou frozen pears.Food chemistry: X · 2026Article
- Application of Image Computing in Non-Destructive Detection of Chinese Cuisine.Foods (Basel, Switzerland) · 2025Review
- Prediction of Key Quality Parameters in Hot Air-Dried Jujubes Based on Hyperspectral Imaging.Foods (Basel, Switzerland) · 2025Article
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Authors and funding
7 authors.
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Abstract
The non-destructive detection of fruit quality is indispensable in the agricultural and food industries. This study aimed to explore the application of hyperspectral imaging (HSI) technology, combined with machine learning, for a quality assessment of pears, so as to provide an efficient technical method. Six varieties of pears were used for inspection, including 'Sucui No.1', 'Zaojinxiang', 'Huangguan', 'Akizuki', 'Yali', and 'Hongli No.1'. Spectral data within the 398~1004 nm wavelength range were analyzed to compare the predictive performance of the Least Squares Support Vector Machine (LS-SVM) models on various quality parameters, using different preprocessing methods and the selected feature wavelengths. The results indicated that the combination of Fast Detrend-Standard Normal Variate (FD-SNV) preprocessing and Competitive Adaptive Reweighted Sampling (CARS)-selected feature wavelengths yielded the best improvement in model predictive ability for forecasting key quality parameters such as firmness, soluble solids content (SSC), pH, color, and maturity degree. They could enhance the predictive capability and reduce computational complexity. Furthermore, in order to construct a quality prediction model, integrating hyperspectral data from six pear varieties resulted in an
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